Document Report Example

Author

Sebastian Ayala Ruano

MicW2Graph

Wastewater treatment (WWT) is the process of removing contaminants from used water before it is discharged back into the environment, which contributes to address water scarcity and to protect aquatic ecosystems. In this project, we investigated the microbiome of WWT to build MicW2Graph, an open-source knowledge graph (KG) that integrates metagenomic and metatranscriptomic information with their biological context, including biological processes, environmental and phenotypic features, chemical compounds, and additional metadata. We developed a workflow to collect meta-omics datasets from MGnify and infer potential interactions among microorganisms through microbial association networks (MANs). MicW2Graph enables the investigation of research questions related to WWT, focusing on aspects such as microbial connections, community memberships, and potential ecological functions.

Descriptive text

Abundance data

Top 5 species by biome (plotly)

Code
import json
import plotly.io as pio

with open('../../example_data/MicW2Graph/top_species_plot_biome.json', 'r') as plot_file:
    plot_data = plot_file.read()

# Load the plot from JSON file
fig_plotly = pio.from_json(plot_data)

# Display the plot
fig_plotly.show()
Figure 1: Top 5 species by biome

Multiline plot (altair)

Code
import altair as alt

with open('../../example_data/altair_multilineplot.json', 'r') as plot_file:
    plot_data = plot_file.read()

fig_altair = alt.Chart.from_json(plot_data)

fig_altair
Figure 2: Multiline plot

Abundance data for all studies (csv)

Code
import pandas as pd
from itables import show
df = pd.read_csv('../../example_data/MicW2Graph/abundance_data_allbiomes.csv', delimiter=',')
show(df)
ERZ794912 ERZ794919 ERZ794932 ERZ794914 ERZ794915 ERZ794916 ERZ794917 ERZ794918 ERZ794920 ERZ794921 ERZ794930 ERZ794931 ERZ781404 ERZ781406 ERZ781405 ERZ795076 ERZ795381 ERZ764804 ERZ778038 ERZ795053 ERZ795067 ERZ795009 ERZ795010 ERZ782916 ERZ782915 ERZ782927 ERZ782929 ERZ782928 ERZ782923 ERZ782930 ERZ782925 ERZ782926 ERZ782924 ERR770579 ERR770580 ERR770581 ERR770582 ERR770583 ERR770584 ERZ1764620 ERZ1764613 ERZ1764614 ERZ1764618 ERZ1764622 ERZ1764624 ERR1726007 ERR1713394 ERR1713395 ERR1713396 ERR1713397 ERR1713398 ERR1726013 ERR1713400 ERR1726017 ERR1713402 ERR1713403 ERR1726026 ERR1713405 ERR1713406 ERR1713407 ERR1713408 ERR1713409 ERR1713410 ERR1726032 ERR1341804 ERR1341803 ERR2808645 ERR2808646 ERR2808647 ERR2808648 ERR2808649 ERR2808650 ERR2808651 ERR2808652 ERR2808653 ERR2808654 ERR2808655 ERR2808656 ERR2808657 ERR2808658 ERR2808659 ERR2808660 ERR2808661 ERR2808662 ERR2808663 ERR2808664 ERR2808665 ERR2808666 ERR2808667 ERR2808668
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Abundance data for all studies (excel)

Code
df2 = pd.read_excel('../../example_data/MicW2Graph/abundance_data_allbiomes.xls')
show(df2)
ERZ794912 ERZ794919 ERZ794932 ERZ794914 ERZ794915 ERZ794916 ERZ794917 ERZ794918 ERZ794920 ERZ794921 ERZ794930 ERZ794931 ERZ781404 ERZ781406 ERZ781405 ERZ795076 ERZ795381 ERZ764804 ERZ778038 ERZ795053 ERZ795067 ERZ795009 ERZ795010 ERZ782916 ERZ782915 ERZ782927 ERZ782929 ERZ782928 ERZ782923 ERZ782930 ERZ782925 ERZ782926 ERZ782924 ERR770579 ERR770580 ERR770581 ERR770582 ERR770583 ERR770584 ERZ1764620 ERZ1764613 ERZ1764614 ERZ1764618 ERZ1764622 ERZ1764624 ERR2729869 ERR2729870 ERR2729871 ERR2729872 ERR2729873 ERR2729874 ERR2729875 ERR2729876 ERR2729877 ERR2729878 ERR2729879 ERR2729880 ERR2729881 ERR2729882 ERR2729883 ERR2729884 ERR2729885 ERR2729886 ERR2729887 ERR2729888 ERR2729889 ERR2729890 ERR2729891 ERR2729892 ERR2729893 ERR2729894 ERR2729895 ERR2729896 ERR2729897 ERR2729898 ERR2729899 ERR2729900 ERR2729901 ERR2729902 ERR2729903 ERR2729904 ERR2729905 ERR2729906 ERR2729907 ERR2729908 ERR2729909 ERR2729910 ERR2729911 ERR2729912 ERR2729913
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